R Notebook

library(tidyverse)

Registered S3 methods overwritten by 'dbplyr':
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  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ───────────────────────────── tidyverse 1.3.1 ──
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✔ tibble  3.1.7     ✔ dplyr   1.0.9
✔ tidyr   1.2.0     ✔ stringr 1.4.0
✔ readr   2.1.2     ✔ forcats 0.5.1
── Conflicts ──────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(tidyverse)
library(shiny)
library(ggplot2)
library(CodeClanData)

Attaching package: ‘CodeClanData’

The following object is masked from ‘package:dplyr’:

    starwars

The following object is masked from ‘package:tidyr’:

    population

library(plotly)

Registered S3 method overwritten by 'data.table':
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  print.data.table     
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout

game_sales %>% 
  mutate(year_of_release = as.factor(year_of_release)) %>% 
  arrange(year_of_release)

sales_genre <- game_sales %>% 
  group_by(genre, year_of_release) %>% 
  summarise(sales = round(mean(sales), 2), .groups = "keep") %>% 
  mutate(year_of_release = as.factor(year_of_release)) %>% 
  arrange(year_of_release) %>% 
  filter(year_of_release == "2012") %>% 
  ggplot(aes(x = genre,
             y = sales,
             fill = "coral"))+
  geom_col()+
  coord_flip()+
  theme(panel.grid = element_blank(),
        panel.background = element_blank(),
        legend.position = "none")+
  labs(x = "Genre",
       y = "Sales (£m)")

ggplotly(sales_genre)

score_genre <- game_sales %>% 
  group_by(genre, year_of_release) %>% 
  summarise(user_score = round(mean(user_score), 2), .groups = "keep") %>% 
  mutate(year_of_release = as.factor(year_of_release)) %>% 
  arrange(year_of_release) %>% 
  filter(year_of_release == "2012") %>% 
  ggplot(aes(x = genre,
             y = user_score,
             fill = "coral"))+
  geom_col()+
  coord_flip()+
  theme(panel.grid = element_blank(),
        panel.background = element_blank(),
        legend.position = "none")+
  labs(x = "Genre",
       y = "User Score")

ggplotly(score_genre)

year_vs_sales <- game_sales %>% 
  group_by(genre, year_of_release) %>% 
  summarise(sales = round(mean(sales), 2), .groups = "keep") %>% 
  filter(genre == "Action") %>% 
ggplot(aes(x = year_of_release,
             y = sales,
             colour = genre))+
  geom_line(show.legend = F)+
  geom_point()+
  theme(panel.grid = element_blank(),
        panel.background = element_blank())+
  labs(x = "Release Year",
       y = "Sales (£m)")

ggplotly(year_vs_sales)

year_vs_score <- game_sales %>% 
  group_by(genre, year_of_release) %>% 
  summarise(user_score = round(mean(user_score), 2), .groups = "keep") %>% 
  filter(genre == "Action") %>% 
  ggplot(aes(x = year_of_release,
             y = user_score,
             colour = genre))+
  geom_line(show.legend = F)+
  geom_point()+
  theme(panel.grid = element_blank(),
        panel.background = element_blank())+
  labs(x = "Release Year",
       y = "User Score")

ggplotly(year_vs_score)